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Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Articles

CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS

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  • Ermawati Ermawati,
  • Idhia Sriliana,
  • Riry Sriningsih
Ermawati Ermawati
Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Alauddin Makassar, Indonesia
Idhia Sriliana
Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Bengkulu, Indonesia
Riry Sriningsih
Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Negeri Padang, Indonesia
DOI: https://doi.org/10.30598/barekengvol17iss3pp1617-1630
Published September 30, 2023
Keywords
  • Clustering,
  • K-Means,
  • K-Medoids,
  • Scientific Publications
How to Cite
[1]
E. Ermawati, I. Sriliana, and R. Sriningsih, “CLUSTERING OF STATE UNIVERSITIES IN INDONESIA BASED ON PRODUCTIVITY OF SCIENTIFIC PUBLICATIONS USING K-MEANS AND K-MEDOIDS”, BAREKENG: J. Math. & App., vol. 17, no. 3, pp. 1617-1630, Sep. 2023.

Copyright (c) 2023 Ermawati Ermawati, Idhia Sriliana, Riry Sriningsih

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

Scientific publication is a measure of the performance of a university. Universities that are owned and operated by the government and whose establishment is carried out by the President of Republic Indonesia are state universities (PTN). One of the efforts that can be made to determine the quantity and quality of state university scientific publications is to conduct PTN clustering based on the productivity of scientific publications. This clustering aims to see the position of state universities in Indonesia into 3 categories, namely “high”, “medium”, and “low”. One of the clustering methods that can be used is cluster analysis. The cluster analysis used in this study is k-means and k-medoids with Silhoutte's validity. Based on the results of the analysis, it was found that the Silhouette k-means value (0.8018) was higher than the Silhouette k-medoids value (0.7281). Therefore, in this case, it can be concluded that the k-means method is better than the k-medoids. The results of cluster analysis using K-Means are 1) PTN with high productivity of scientific publications, namely ITB, ITS, UGM, and UI. The four PTNs are PTN as Legal Entity (PTN-BH) located in Java, 2) PTN with medium scientific publication productivity consists of 16 PTN which were dominated by PTN-BH and PTN as Public Service Board (PTN-BLU) with the largest location in Java, and 3) PTN with low scientific publication productivity consisted of 102 PTN which were dominated by PTN as general state financial management (PTN-Satker) with most locations outside Java.

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